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Perception for visualization: From design to evaluation

Perception for visualization: From design to evaluation. Victoria Interrante, U. Minn. Haim Levkowitz (chair), IVPR, UMass Lowell Hans-Peter Meinzer, DKFZ-Heidelberg, Germany. Overview. Purpose of collecting information Constantly growing ... Need to understand how to ... Challenges …

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Perception for visualization: From design to evaluation

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  1. Perception for visualization: From design to evaluation Victoria Interrante, U. Minn. Haim Levkowitz (chair), IVPR, UMass Lowell Hans-Peter Meinzer, DKFZ-Heidelberg, Germany

  2. Overview • Purpose of collecting information • Constantly growing ... • Need to understand how to ... • Challenges … • Examples ...

  3. Purpose of collecting information • Present it to • Analyze it by • Human beings • Constantly growing: amounts & complexity ...

  4. Constantly growing ... • Amounts collected • Complexity • Information • Analysis • Need to understand how to ...

  5. Need to understand how to ... • Increase comprehension • Make analysis easier & efficient

  6. A presentation … • An experience, not just an image ... • Aimed at humans • Has to be designed ...

  7. An experience, not just an image ... • User-directed, dynamic • Multidisciplinary … • Multisensory ...

  8. Multidisciplinary ... • Graphics • Imaging • Psychology • Man-machine interface • Databases • Multimedia

  9. Multisensory ... • Visual • Auditory • Tactile • More?

  10. Has to be designed ... • Audience • Question(s) / tasks ... • Contents • Delivery

  11. Question(s) / tasks ... • Explore vs. Confirm • E.g., detection vs. identification

  12. Perceptually-based presentation … • Make perceptually effective … • How? ...

  13. Make perceptually effective … • Understand perceptual processes … • Harness them ...

  14. Understand perceptual processes ... • Color • Size • Shape • Texture • Motion • Sound

  15. Harness them ... • Data "Speak" for themselves • Exploratory vs. confirmatory analyses • Raise dimensionality • MDMV

  16. How? ... • Substitute pre-conscious for conscious • Pre-conscious … • Conscious ...

  17. Pre-conscious ... • Do not interfere with ability to think • Parallel • Hard-wired/entrained • Fast • Relentless • Simultaneously w/conscious analysis

  18. Conscious ... • Interfere • Serial • Ad-hoc • Slow • Cause fatigue • Distract from higher level analysis

  19. Challenges … • Understand "perception" ... • Apply perception ... • Truth and accuracy obligation … • Effectiveness • Evaluation and verification ...

  20. Understand "perception" ... • Lower-level vision (also audition, touch) • Perception … • Cognition … • Aesthetics • Emotional & cultural

  21. Perception ... • Color • Shape • Size • Texture • Motion • Segmentation

  22. Cognition ... • Attention (pre-attentive vs. scrutiny; preconscious vs. conscious) • Memory (iconic, short term, long term) • Semantics & symbolism

  23. Apply perception ... • ==> Perceptually-based • Representations … • Rules & guidelines … • Interaction • ==> Presentations

  24. Representations ... • E.g., generalize pixels fi more info • Visual • Color • Geometry … • Motion • Sound

  25. Geometry ... • Line orientation • Area • Volume • Curvature • Texture

  26. Rules & guidelines ... • Contrast • Color • Font • Size • Style

  27. Truth and accuracy obligation ... • Informing vs. entertaining • Comprehension vs. aesthetics • Informing ==> as truthful as possible • Avoid misleading viewers • E.g., Brooks (Vis '93): background music ...

  28. E.g., Brooks (Vis '93): background music ... • What message? • Lower SNR, unless • Intentional • Accurate • Show business

  29. Evaluation and verification … • Tasks … • Data ...

  30. Tasks ... • Presence / absence • Edge / boundary • Target / blob • Location • Classification

  31. Data ... • Real … • Synthesized ...

  32. Real ... • "Truth" unknown • E.g., • Satellite • Water vs. land • Medical • Normal vs. abnormal

  33. Synthesized ... • "Truth" known • Statistics • Not real

  34. Examples ... • Medical • Earth science • Reconnaissance • Presentations

  35. Conclusions ... • What is information presentation • Perceptually-based information presentation • Design • Evaluation & verification • Challenges • Examples

  36. Moral ... • Present to inform, not to impress; if you inform, you will impress after Fred Brooks Keynote Address Visualization ’93

  37. Emerging discipline • Eick: "Graphically displaying text" • J. Comp. & Graphical Statistics • Four progress stages … • Wong & Bergeron, 1995: "30 years of MDMV Vis” • Four stages of MDMV Vis. Development ...

  38. Four progress stages ... • Skilled artisans: Craft • Practical experience guidelines • Researchers: Scientific principles & theories • Engineers: Production rules • Widely available technology

  39. Four stages of MDMV Vis. Development ... • Pre-1976: The Searching Stage • 1977-1985: The Awakening Stage • 1986-1991: The Discovery Stage • 1992-present: The Elaboration Stage

  40. Tutorial overview • Levkowitz: Vision & color; evaluation and verification • Interrante: Visual perception; cognitive issues • Meinzer: Perception in image generation and understanding • Summary, discussions, future directions

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